Engaging end-user driven recommender systems: personalization through web augmentation

Abstract

In the past decades recommender systems have become a powerful tool to improve personalization on the Web. Yet, many popular websites lack such functionality, its implementation usually requires certain technical skills, and, above all, its introduction is beyond the scope and control of end-users. To alleviate these problems, this paper presents a novel tool to empower end-users without programming skills, without any involvement of website providers, to embed personalized recommendations of items into arbitrary websites on client-side. For this we have developed a generic meta-model to capture recommender system configuration parameters in general as well as in a web augmentation context. Thereupon, we have implemented a wizard in the form of an easy-to-use browser plug-in, allowing the generation of so-called user scripts, which are executed in the browser to engage collaborative filtering functionality from a provided external rest service. We discuss functionality and limitations of the approach, and in a study with end-users we assess the usability and show its suitability for combining recommender systems with web augmentation techniques, aiming to empower end-users to implement controllable recommender applications for a more personalized browsing experience.

Similarity Measures for Recommender Systems: Drawbacks and Neighbors Formation

“…3. The common ratings between (𝑢𝑢 2 , 𝑢𝑢 6 ) are (5,4,3,5) 8. MSD assigned a moderate value for the two extreme users 𝑢𝑢 11 and 𝑢𝑢 15 , which is better than PCC and COS.…”

“…9. The common history between 𝑢𝑢 2 and 𝑢𝑢 5 is (2,1,2,2) and (5,4,3,5), showing opposite mood users. However, COS gave them a very high mark of 0.961 due to the free value problem (PB10).…”

“…Hence, it helps active users to find relevant and exciting information in the form of suggestions, and therefore, they have a direct effect on the correctness of the generated list. Actually, getting a representative similarity value will enhance the system's personalization level and achieve the primary goal of such systems [5].…”

“…The opposite user for this case is User 4, 𝑢𝑢 4 . At the extreme points, user 5, 𝑢𝑢 5 , has pure negative ratings (1,2) while user 6, 𝑢𝑢 6 , is the opposite with pure positive ratings(4,5). User 7, 𝑢𝑢 7 , represents a colinear vector with 𝑢𝑢 5 by a factor of 2.…”

Collaborative web extensions: a P2P approach

“…Some well-known techniques such as mash-ups [3], [4] and web augmentation [5] rest on web extensions. Their purpose may be quite broad, such as integrating web contents [6], supporting repetitive users tasks [7], [8], improving accessibility [9], [10], and recommendation-based personalization [11] among other possibilities. Besides attracting the interest of the research community as shown by the previous examples, web-extensions are popular among web users that desire to adapt the web.…”